Author

Kaori Muto

Institute of Medical Science, The University of TokyoORCID 0000-0001-5286-4706Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Genetic & Genomic Medicine· 1 Genomics· 1

Signature Assays, Organisms & Methods

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Research Focus:fine-mapping· 2 Assay:GWAS summary statistics· 2 Organism:human· 2 Approach:statistical model· 2 Research Focus:benchmarking· 1 Research Focus:GWAS / association mapping· 1 Approach:machine learning (classical)· 1 Research Focus:polygenic risk· 1

Frequent Co-Authors

Akihide Masumoto· 2 shared Akiko NagaiiD· 2 shared Daisuke Obata· 2 shared Hilary K. FinucaneiD· 2 shared Hiroki YamaguchiiD· 2 shared Kazuhisa TakahashiiD· 2 shared Ken YamajiiD· 2 shared Koichi MatsudaiD· 2 shared

Activity by Year

2021-2022 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 45 mutual co-author links

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2 shared papersGenetic & Genomic Medicine

Akihide Masumoto

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2 papers

Recent·Most cited

Genetic & Genomic MedicineNature GeneticsPeer-reviewedApr 1, 2022

Leveraging fine-mapping and non-European training data to improve cross-population polygenic risk scores

Omer Weissbrod, Masahiro Kanai, Huwenbo Shi…·Alkes L. Price

This paper introduces PolyPred and PolyPred+ to improve cross-population polygenic risk score accuracy by addressing LD, MAF, and causal effect size differences using fine-mapping and non-European training data.

264 citationstop 1%

humanGWAS summary statistics

𝕏 PostBluesky

GenomicsNature CommunicationsPeer-reviewedJun 7, 2021

Leveraging supervised learning for functionally-informed fine-mapping of cis-eQTLs identifies an additional 20,913 putative causal eQTLs

Qingbo S. Wang, David R. Kelley, Jacob C. Ulirsch…·Hilary K. Finucane

The paper uses supervised learning to fine-map cis-eQTLs, identifying 20,913 additional putative causal eQTLs and 310 candidate genes.

89 citationstop 1%

humanGWAS summary statistics

𝕏 PostBluesky